In this paper, an innovative multi-task Bayesian Compressive Sensing (MT-BCS)-based approach is proposed to image sparse metallic objects. Towards this end, the problem of estimating the position of the contrast currents is formulated in a Bayesian framework, and a thresholding procedure is applied to derive the binary function describing the scatterer profile. A preliminary set of numerical examples is presented to assess the effectiveness of the considered methodology.
Imaging PEC through innovative compressive sensing approaches
Oliveri, Giacomo;Rocca, Paolo;Poli, Lorenzo;Massa, Andrea
2013-01-01
Abstract
In this paper, an innovative multi-task Bayesian Compressive Sensing (MT-BCS)-based approach is proposed to image sparse metallic objects. Towards this end, the problem of estimating the position of the contrast currents is formulated in a Bayesian framework, and a thresholding procedure is applied to derive the binary function describing the scatterer profile. A preliminary set of numerical examples is presented to assess the effectiveness of the considered methodology.File in questo prodotto:
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